Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| OLS ya Fourier (Fourier-Augmented Ordinary Least Squares)× | OLS yenye Vigezo Vinavyobadilika kwa Wakati (TVP-OLS)× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2004 | 1976 |
| Mwanzilishi≠ | Becker, Enders, and Hurn | Cooley & Prescott (1976); further developed by Harvey (1990) |
| Aina≠ | Augmented linear regression | Time-series regression with evolving coefficients |
| Chanzo asilia≠ | Becker, R., Enders, W., & Hurn, S. (2004). A general test for time dependence in parameters. Journal of Applied Econometrics, 19(7), 899–906. DOI ↗ | Cooley, T. F., & Prescott, E. C. (1976). Estimation in the Presence of Stochastic Parameter Variation. Econometrica, 44(1), 167–184. DOI ↗ |
| Majina mbadala | Fourier OLS, Fourier-augmented OLS, trigonometric OLS, smooth structural break OLS | TVP-OLS, time-varying coefficient regression, rolling OLS, locally weighted OLS |
| Zinazohusiana≠ | 6 | 4 |
| Muhtasari≠ | Fourier OLS is an OLS regression extended by adding low-frequency trigonometric (sine and cosine) terms to the regressor matrix. These Fourier components approximate smooth, gradual structural changes in the regression relationship over time without requiring knowledge of the number, timing, or form of the breaks. | Time-Varying Parameter OLS extends classical ordinary least squares to allow regression coefficients to change over time. Instead of assuming fixed slopes throughout the sample, the model treats each coefficient as a stochastic process, tracking how economic relationships evolve — making it well-suited for analysing structural change in time-series data. |
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